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Post-grouting is an effective engineering technique for enhancing the bearing performance of pile foundations, and the grouting material is a key factor determining the performance of post-grouted piles. In this study, different grouting materials (fly ash-cement, geopolymer, cement, and cement-sodium silicate) were adopted in laboratory tests. Model tests of post-grouted piles were conducted under simulated groundwater flow conditions and long-term loading scenarios to investigate the influence of various grouting materials on pile bearing performance under vertical loading. The research focuses on the characteristics of load- settlement curves, the axial force transfer along the pile, and the mobilization behavior of side resistance and tip resistance, and employs scanning electron microscopy (SEM) tests to reveal the action mechanisms of different grouting materials. The test results indicate that the grouting process significantly enhances the pile-soil interaction. Compared with the ungrouted piles, the bearing capacity of the grouted piles increases markedly. Under flowing-water conditions, the cement-sodium silicate double-liquid grout provides the best reinforcement effect around the pile due to its short gel time, rapid solidification, and dense hydration products, whereas the fly ash-cement and geopolymer grouts mainly improve the soil via permeation grouting, with the hydration reaction of fly ash proceeding relatively slowly.

Traditional cement-based grouting, widely used to restore soil resistance around piles, poses serious environmental concerns due to its high CO2 emissions. To address this issue, this study introduces natural gum biopolymers as sustainable alternatives for pile grouting. For the first time, Persian and Arabic gums, both hydrocolloid biopolymers, were applied to improve the performance of tapered helical piles installed in poorly graded sand. Direct shear tests were conducted using a Taguchi experimental design, varying gum type, content, and curing time. The analysis revealed that Persian gum provided superior improvements in cohesion and internal friction angle compared to Arabic gum. Based on these results, Persian gum was selected for pile load tests. Axial loading experiments were then performed in the Frustum Confining Vessel (FCV) on tapered helical piles with different helix-to-diameter ratios. The results showed that gum injection increased both the ultimate bearing capacity and initial stiffness of tapered helical piles. Among the investigated configurations, the pile with closer helix spacing (λ = 1.5) exhibited the highest load-bearing performance due to enhanced interaction between adjacent helices and the surrounding soil. SEM observations confirmed that gums improved soil microstructure by forming hydrocolloid bridges between sand particles and increasing interparticle friction. Overall, the study demonstrates that natural gums can serve as effective, eco-friendly substitutes for cement, significantly enhancing pile performance.

Since traditional empirical methods frequently fail to capture complex soil-pile interactions, accurately evaluating the bearing capacity of driven piles remains a critical yet difficult task in geotechnical engineering. In this regard, this study proposes a novel machine learning framework for predicting the pile bearing capacity (PBC) of driven piles using a stacking guided by a multi-objective Pareto optimization approach. Using a comprehensive dataset that includes 472 records from the literature, the framework combines random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost) to increase predictive accuracy and generalizability. The dataset consists of a range of geological, geometric, and loading conditions, in addition to explanatory input features from the standard penetration test (SPT) dataset, such as pile diameter, soil depth, different soil layers, and the SPT-N values. Model interpretability is achieved through Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP), revealing key influencers such as pile diameter and SPT-N values along the shaft and tip. The stacking model improved predictive accuracy over the respective base-level models, using a Pareto-optimized stacking model, achieving a coefficient of determination of 0.9471. It also had reduced mean squared error (MSE) and mean absolute error (MAE) on the testing dataset as compared to the base-level models. Sensitivity and parametric analyses of the PBC confirmed that the model calculations were significant and demonstrated that pile diameter and soil layer thicknesses influence PBC. Finally, this approach provides geotechnical engineers with an accurate and interpretable tool to predict PBC, supporting safer and more cost-effective pile foundation designs in complex soil conditions.

The circular helicoid (CH) pile is a novel special-shaped pile developed over the past decade and widely used as an uplift-resistant foundation for photovoltaic support structures due to its superior uplift performance. However, the mechanisms by which ground conditions influence the mobilization of uplift bearing capacity remain unclear, limiting design optimization. In this study, a three-dimensional finite element model of CH pile foundations under axial uplift loading was established in ABAQUS and validated against seven sets of in-situ uplift load tests. Uplift bearing capacities were determined from the pile-head load-displacement response using the displacement control and double-tangent methods. Parametric and sensitivity analyses were performed to evaluate the effects of key soil parameters, including Young's modulus E, Poisson's ratio ν, cohesion c, internal friction angle φ, and the pile-soil interface friction coefficient µ. Results show that the uplift force-displacement relationship exhibits a smooth, progressive response, and the pile-head displacement at the ultimate limit state is approximately 0.1 times the pile diameter. Uplift bearing capacities increase positively with all considered parameters. The recommended parameter importance ranking is c > E > φ > ν > µ at the small-displacement stage, and c > φ > E > ν > µ at the large-displacement stage.

This study systematically investigated the dynamic diversity, potential sources, and antifungal activities of small molecular peptides during the pile-fermentation process of post-fermented tea. By analyzing the damaging effects of small molecular peptide extracts from tea samples at different pile-fermentation stages on the spore cell membranes of Aspergillus carbonarius (A. carbonarius) and the inhibitory activity against β-1,3-glucan synthase (β-1,3-GS), it was confirmed that some small molecular peptides exhibit significant antifungal effects. The main findings are as follows: (1) The number of identified small molecular peptides showed a trend of first increasing and then decreasing with the progress of pile-fermentation, peaking at 4453 species on the 35th day of pile-fermentation, and were dominated by hexapeptides and heptapeptides with molecular weights ranging from 600 to 800 Da. (2) Based on orthogonal partial least squares discriminant analysis (OPLS-DA), the samples were divided into three characteristic stages according to the differences in small molecular peptide composition at different stages, and 156 characteristic peptides with a relative abundance higher than 0.1% were screened out. Their precursor proteins were derived from 148 proteins belonging to 16 genera, including Camellia, Aspergillus, Saccharomyces, Penicillium, and Bacillus. (3) BLAST alignment results showed that five out of the 156 characteristic peptides were degradation fragments of known antifungal peptides originating from Aspergillus and Bacillus. (4) Combining molecular docking screening and in vitro verification of synthetic peptides, a total of 27 small molecular peptides with antifungal activity were obtained, and their mechanism of action was the inhibition of β-1,3-GS activity. (5) The small molecular peptides related to antifungal activity could be classified into two categories: enzymatic hydrolysates of known antifungal peptides, and the enzymatic hydrolysates of tea-derived proteins or macromolecular peptides. Both categories were mainly distributed in the three stages of pile-fermentation, and there was a significant positive correlation among the population size of dominant microorganisms, microbial peptidase activity, and the abundance of small molecular peptides. This study reveals the dynamic generation pattern and antifungal potential of small molecular peptides during the pile-fermentation of post-fermented tea, providing a new scientific basis for evaluating the dynamic changes in microbial communities in tea and effectively controlling the contamination of harmful fungi during the pile-fermentation process.

This research compares theoretical methods with static pile load test results to identify the most effective approaches for determining pile capacity across various soil and pile types, while also proposing modifications to the Bangladesh National Building Code (BNBC) 2020 guidelines. The study involved broad data collection from pile load test reports and subsoil investigations across six projects, focusing precast and cast-in-situ piles. Theoretical analyses were performed using i) bearing capacity equations as per BNBC 2020 (α-method and β-method), ii) Standard Penetration Test (SPT)-based BNBC 2020 method, and iii) SPT-based other methods from literature including Meyerhof, and Shioi Fukui equations. Following ASTM D1143 guidelines, eight evaluation methods were used to interpret load-settlement data from static load test. Statistical analyses involving Mean Absolute Percentage Error (MAPE), Bias Factor (λ), and Coefficient of Variation (COV) were conducted to evaluate predictive accuracy. Findings indicate that for clay soil, the SPT-based equations of Shioi & Fukui for skin friction and Meyerhof for end bearing can be considered as suitable alternatives for future BNBC revisions, showing lower variation with actual capacity. For sandy, silty, and layered soil devoid of clay, the SPT-based equations from BNBC 2020 are deemed appropriate.

In nuclear spectroscopy, a physical phenomenon known as the pile-up effect distorts direct measurements by causing temporal overlap of detector pulses. Existing deep learning-based pile-up correction methods rely heavily on supervised training with simulated data, which often generalize poorly to real measurements due to simulation-experiment discrepancies. In this work, we propose a contrastive learning framework to learn robust and transferable representations directly from large-scale unlabeled real nuclear pulse signals. The detector output is segmented into physically complete pulse aggregations using a zero-crossing-based strategy, which serve as semantically coherent instances for representation learning. Physics-inspired data augmentations are designed to realistically model detector noise and bandwidth effects while preserving pulse area. A one-dimensional ResNet encoder is employed for efficient representation learning. The learned representations are transferred to pile-up identification and counting-rate estimation tasks. Experimental results on real nuclear radiation detection systems demonstrate that our method achieves strong performance and robustness under high counting-rate conditions, with particularly pronounced advantages in challenging peak pile-up scenarios.

Prompt 3D mapping of grain storage is essential for effective management. However, standard mapping algorithms encounter a number of challenges, with the typical granary environment containing dust, grain piles, and narrow aisles. A single robotic agent is not able to provide complete area coverage, and most multi-robot approaches involve re-scanning the same areas due to a lack of explicit viewpoint-based task allocation processes. In order to overcome the above issues, we propose an air-ground collaborative exploration system for complex multi-grain pile scenarios. Exploration redundancy can be reduced by estimating the advantages of viewpoints through ray tracing and assigning the tops of the grain piles to aerial robots with ground vehicles in lower regions and narrow aisles. In order to manage dense dust (5-15 mg/m3), the quality-aware fusion strategy evaluates the reliability of the distance and point density of the sensing to reduce the influence of degraded aerial depth data. Moreover, mapping relies on LiDAR data to ensure mapping quality. A mechanism for re-scanning to enable coverage-driven exploitation of insufficiently explored regions is subsequently proposed. The simulation results show that the design achieved a grain pile coverage of 97.2%, with the total exploration time reduced by 20.1% over single-robot baselines. The results indicate that viewpoint-aware task allocation and dust-sensitive perception fusion can offer a practical solution for autonomous inspection in GPS-restricted, dust-rich industrial environments, such as granary facilities.

This study presents one of the first facility-wide assessments of greenhouse gas (CH4, N2O, CO2) and air pollutant (NH3, H2S, NMVOCs) emissions from a full-scale covered aerated static pile (CASP) composting facility in Napa, California. Emissions were quantified using dynamic flux chambers and unmanned aerial vehicle (UAV)-based flux curtain (CH4 only) methods during four seasonal campaigns. Facility-wide climate forcing emissions were 66,000 ± 5,000 Mg CO2-eq. yr-1 (0.55 ± 0.04 Mg CO2-eq. Mg-1 wet waste), dominated by CO2 (approximately 85%), with smaller contributions from CH4 (approximately 10%), N2O (approximately 2%), and NMVOCs (approximately 3%). The CASP phase was the main source of CH4 (65%), followed by curing (28%) and feedstock piles (4%). Fluxes were primarily governed by operational phase, with secondary effects from season and spatial variabilities. UAV-based CH4 estimates were approximately 50% lower than chamber extrapolations, highlighting bias when scaling point measurements to facility level. Results provide updated emission factors for aerobic composting and identify operational drivers-including moisture, aeration uniformity, and pile geometry-that strongly influence emissions. The findings inform best practices for composting operations and support improved greenhouse gas inventories, life cycle assessments, and climate policies.

The aim of this study was to investigate if the presence of a prominent sentinel pile (SP) had any impact on the treatment success of botulinum toxin (BT) injection, as well as the clinical presentation of patients with chronic anal fissure (CAF). Patients with CAF with or without prominent sentinel piles underwent BT injection. In addition to objective healing, a detailed symptom severity score (REALISE) immediately before and 6 months after BT injection was recorded. This was a retrospective, single-center observational cohort study including consecutive patients treated in a specialized proctology unit. Of the 249 patients, 68 presented with prominent SP (27.3%). The overall objective healing rate among all patients who received a single injection of BT was found to be 74.7% at 2 months. When stratified, age distribution was similar between patients with (SP+) and without SP (SP-) (p&#x2009;=&#x2009;0.545). However, SP was more prevalent in female patients (p&#x2009;=&#x2009;0.009). The objective healing rates after a single BT injection were 80.7% in the SP- group and 58.8% in the SP+&#x2009;group (p&#x2009;=&#x2009;0.001). Pre-treatment REALISE scores were significantly reduced in both groups after BT injection (p&#x2009;<&#x2009;0.001 for both). However, post-treatment scores were higher in the SP+&#x2009;group compared with the SP- group (p&#x2009;<&#x2009;0.001). Multivariable analysis confirmed SP presence as an independent predictor of reduced objective healing after BT injection. Even with the SP, a considerable proportion of patients with CAF heal after BT treatment and their symptoms are generally relieved. However, symptomatic improvement is less marked and the objective healing rates are lower in the SP+&#x2009;group. The presence of SP may, therefore, negatively influence the clinical effectiveness of BT treatment of CAF. These findings should be interpreted in light of the retrospective single-center design and the potential for selection and recall bias.

Earthquakes caused major structural damage, prompting the development of seismic isolation techniques. On the other hand, building structures on weak soils poses many challenges due to their lack of bearing capacity and inappropriate behavior in the long term and during earthquakes. This study introduces a new QZS device called the Pendulum Column with Inclined Negative Stiffness (PC-INS) which increases the system&#x2019;s natural period, enhancing isolation performance. A key advantage of PC-INS is its cylindrical outer shell, which acts as a pile when embedded in soil. This feature significantly improves performance in weak or inappropriate soil. Overall, PC-INS offers a promising solution for effective seismic isolation in both standard and challenging geotechnical environments. A model was analyzed using multiple earthquake records in MATLAB and validated through finite element modeling in ABAQUS. The soil-structure interaction was studied and a 3D model was developed to investigate the stress distribution. Results demonstrate that PC-INS reduces both acceleration and displacement. The system also performs reliably with minimal damping (5%). FFT analysis confirmed the absence of resonance in almost all analyzed earthquakes. The input energy analysis demonstrated the effectiveness of the system. Proposed hybrid pile demonstrates a significant reduction in responses generated in the soil. The structural model revealed a considerable reduction in the structural response when the proposed hybrid pile-isolator were employed.